A hierarchical spatial-temporal graph-kernel for high-resolution SAR image change detection

References

• Aiazzi, B., L. Alparone, S. Baronti, A. Garzelli, and C. Zoppetti. 2013. “Nonparametric Change Detection in Multitemporal SAR Images Based on Mean-shift Clustering.” IEEE Transactions on Geoscience and Remote Sensing 51 (4): 2022–2031. doi:10.1109/TGRS.2013.2238946.
   Web of Science ®Google Scholar
• Bruzzone, L., and D. F. Prie. 2000. “Automatic Analysis of the Difference Image for Unsupervised Change Detection.” IEEE Transactions Geoscience and Remote Sensing 38 (3): 1171–1182. doi:10.1109/36.843009.
   Web of Science ®Google Scholar
• Camps-Valls, G., L. Gomez-Chova, J. Munoz-Mari, J. L. Rojo-Alvarez, and M. Martinez-Ramon. 2008. “Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 46 (6): 1822–1835. doi:10.1109/tgrs.2008.916201.
   Web of Science ®Google Scholar
• Camps-Valls, G., N. Shervashidze, and K. M. Borgwardt. 2010. “Spatial-Spectral Remote Sensing Image Classification with Graph Kernels.” IEEE Geoscience and Remote Sensing Letters 7 (4): 741–745. doi:10.1109/lgrs.2010.2046618.
   Web of Science ®Google Scholar
• Chatelain, F., J. Y. Tourneret, and J. Inglada. 2008. “Change Detection in Multisensory SAR Images Using Bivariate Gamma Distributions.” IEEE Transactions on Image Processing 17 (3): 249–258. doi:10.1109/TIP.2008.916047.
   PubMed Web of Science ®Google Scholar
• Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46. doi:10.1046/j.1365-2249.2000.01137.x.
   Web of Science ®Google Scholar
• Cristianini, N., and J. Shawe-Taylor. 2000. An Introduction to Support Vector Machines and Others Kernel Based Learning Methods. Cambridge, U.K.: Cambridge Univ. Press. doi:10.1108/k.2001.30.1.103.6.
   Google Scholar
• Gong, M., M. Jia, L. Su, S. Wang, and L. Jiao. 2014. “Detecting Changes of the Yellow River Estuary via SAR Images Based on a Local Fit-Search Model and Kernel-Induced Graph Cuts.” International Journal of Remote Sensing 35 (11–12): 4009–4030. doi:10.1080/01431161.2014.916054.
   Web of Science ®Google Scholar
• Gong, M., H. Yang, and P. Zhang. 2017. “Feature Learning and Change Feature Classification Based on Deep Learning for Ternary Change Detection in SAR Images.” ISPRS Journal of Photogrammetry and Remote Sensing 129: 212–225. doi:10.1016/j.isprsjprs.2017.05.001.
   Web of Science ®Google Scholar
• Hsu, C. W., C. C. Chang, and C. J. Lin. 2003. A Practical Guide to Support Vector Classification. Taipei, Taiwan, Technique Report: Deptartment of Compututer Science National Taiwan University.
   Google Scholar
• Hu, W., B. Wu, P. Wang, C. Yuan, Y. Li, and S. Maybank. 2018. “Context-Dependent Random Walk Graph Kernels and Tree Pattern Graph Matching Kernels with Applications to Action Recognition.” IEEE Transactions on Image Processing 27 (10): 5060–5075. doi:10.1109/TIP.2018.2849885.
   Web of Science ®Google Scholar
• Jia, L., M. Li, P. Zhang, and Y. Wu. 2016a. “SAR Image Change Detection Based on Correlation Kernel and Multistage Extreme Learning Machine.” IEEE Transactions on Geoscience and Remote Sensing 54 (10): 5993–6006. doi:10.1109/TGRS.2016.2578438.
   Web of Science ®Google Scholar
• Jia, L., M. Li, P. Zhang, Y. Wu, L. An, and W. Song. 2016b. “Remote-Sensing Image Change Detection with Fusion of Multiple Wavelet Kernels.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (8): 3405–3418. doi:10.1109/JSTARS.2015.2508043.
   Web of Science ®Google Scholar
• Johnson, R., and T. Zhang. 2008. “Graph-Based Semi-Supervised Learning and Spectral Kernel Design.” IEEE Transactions on Information Theory 54 (1): 275–288. doi:10.1109/TIT.2007.911294.
   Web of Science ®Google Scholar
• Liu, J., M. Gong, K. Qin, and P. Zhang. 2018. “A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.” IEEE Transactions on Neural Networks and Learning Systems 29 (3): 545–559. doi:10.1109/TNNLS.2016.2636227.
   PubMed Web of Science ®Google Scholar
• Lu, D., E. Moran, and S. Hetrick. 2011. ““Detection of Impervious Surface Change with Multitemporal Landsat Images in an Urban–Rural Frontier.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (1): 298–306. doi:10.1016/j.isprsjprs.2010.10.010.
   PubMedGoogle Scholar
• Martinis, S., A. Twele, and S. Voigt. 2011. “Unsupervised Extraction of Flood-induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs.” IEEE Transactions on Geoscience and Remote Sensing 49 (1): 251–263. doi:10.1109/tgrs.2010.2052816.
   Web of Science ®Google Scholar
• Moreira, A., P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek, and K. P. Papathanassiou. 2013. “A Tutorial on Synthetic Aperture Radar.” IEEE Geoscience and Remote Sensing Magazine 1 (1): 6–43. doi:10.1109/GRS.2013.2248301.
   Web of Science ®Google Scholar
• Pham, M., G. Mercier, and J. Michel. 2016. “Change Detection between SAR Images Using a Pointwise Approach and Graph Theory.” IEEE Transactions on Geoscience and Remote Sensing 54 (4): 2020–2032. doi:10.1109/TGRS.2015.2493730.
   Web of Science ®Google Scholar
• Scholkopf, B., and A. Smola. 2002. Learning with Kernels-Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, MA, USA: MIT Press. doi:10.1109/TNN.2005.848998.
   Google Scholar
• Shawe-Taylor, J., and N. Cristianini. 2004. Kernel Methods for Pattern Analysis. Cambridge, U.K.: Cambridge Univ. Press. doi:10.1198/jasa.2006.s153.
   Google Scholar
• Singh, A. 1989. “Digital Change Detection Techniques Using Remotely Sensed Data.” International Journal of Remote Sensing 10 (6): 989–1003. doi:10.1080/01431168908903939.
   Web of Science ®Google Scholar
• Vakalopoulou, M., K. Karantzalos, N. Komodakis, and N. Paragios. 2016. “Graph-Based Registration, Change Detection, and Classification in Very High Resolution Multitemporal Remote Sensing Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (7): 2940–2951. doi:10.1109/JSTARS.2016.2557081.
   Web of Science ®Google Scholar
• Vidal, A., and M. R. Moreno. 2011. “Change Detection of Isolated Housing Using a New Hybrid Approach Based on Object Classification with Optical and Terra SAR-X Data.” International Journal of Remote Sensing 32 (24): 9621–9635. doi:10.1080/01431161.2011.571297.
   Web of Science ®Google Scholar
• Wang, J., X. Yang, and L. Jia. 2018. “Pointwise SAR Image Change Detection Based on Stereograph Model with Multiple-span Neighbourhood Information.” International Journal of Remote Sensing 40 (1): 31–50. doi:10.1080/01431161.2018.1488284.
   Web of Science ®Google Scholar
• Zhang, Y., X. Qiu, B. Lei, K. Fu, F. Li, and C. Ding. 2016. “The Shadow Enhancement for Targets with Flat Structures in SAR Images.” 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China. doi: 10.1109/IGARSS.2016.7729233.
   Google Scholar
• Zhao, Q., J. Ma, M. Gong, and H. Li. 2016. “Three-class Change Detection in Synthetic Aperture Radar Images Based on Deep Belief Network.” Journal of Computational and Theoretical Nanoscience 13 (6): 3757–3762. doi:10.1166/jctn.2016.5208.
   Google Scholar
• Zheng, Y., L. Jiao, H. Liu, X. Zhang, B. Hou, and S. Wang. 2017. “Unsupervised Saliency-Guided SAR Image Change Detection.” Pattern Recognition 61: 309–326. doi:10.1016/j.patcog.2016.07.040.
   Web of Science ®Google Scholar
• Zheng, Y., X. Zhang, B. Hou, and G. Liu. 2014. “Using Combined Difference Image and K-means Clustering for SAR Image Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 11 (3): 691–695. doi:10.1109/LGRS.2013.2275738.
   Web of Science ®Google Scholar